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A special issue of Molecules (ISSN 1420-3049). This special issue belongs to the section "Molecular Diversity".

Deadline for manuscript submissions: closed (15 October 2015) | Viewed by 116365

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Prof. Dr. Peter Willett

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Information School, The University of Sheffield, Regent Court, 211 Portobello, Sheffield S1 4DP, UK
Interests: bibliometrics, bibliometric methods for the evaluation of research productivity; chemoinformatics, in particular the use of clustering, graph theory, and machine learning methods for the processing of databases of chemical and biological structures
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Special Issue Information

Dear Colleagues,

Chemistry is, and has been for many years, one of the most information-rich academic disciplines. The very first journal devoted to chemistry was published as early as 1778, and the literature has grown steadily since then. Much of the information in chemistry relates to the structures—in both two and three dimensions—of individual chemical molecules; for example, the world’s largest chemical database, the Chemical Registry, produced by Chemical Abstracts Service, now contains the structures of over 90 million distinct molecules, and there are additional millions in other public databases and in the corporate files of pharmaceutical, agrochemical, and biotechnology companies. This wealth of information has spurred the development of a specialist discipline, that of chemoinformatics, which “encompasses the design, creation, organization, storage, management, retrieval, analysis, dissemination, visualization and use of chemical information” [1].

The structure of a molecule is a prime factor in determining its physical, chemical, and biological properties, and chemoinformatics draws on techniques from areas such as graph theory, multivariate statistics, and machine learning to provide sophisticated data mining facilities to correlate such properties with structure. This Special Issue of Molecules welcomes contributions on all aspects of chemoinformatics, such as (but by no means limited to):

Virtual Screening (e.g., docking and pharmacophore analysis, similarity and clustering methods, machine learning);
Computational methods for lead identification and optimization (e.g., modeling and structure-activity methods, ADMET prediction, de novo design);
High-throughput screening (e.g., assay quality control, design of screening collections);
New algorithms and technologies (e.g., distributed processing, cloud computing, open source chemoinformatics software, visualization);
Emerging applications (e.g., polypharmacology, chemical text mining, target drugability)
Case studies of the implementation of any of the above.

The issue welcomes original research articles, work in progress, surveys, reviews, and viewpoint articles.

[1] Warr, W.A. Balancing the needs of the recruiters and the aims of the educators (1999). Paper presented at the 218th American Chemical Society National Meeting, New Orleans, USA.

Prof. Dr. Peter Willett
Guest Editor

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Keywords

activity cliffs
CADD
CAMD
chemical data mining
chemical text mining
computer-aided drug discovery
computer-aided molecular design
computer-aided synthesis design
docking
ligand-base virtual screening
molecular diversity analysis
molecular similarity
pharmacophore mapping
property prediction
similarity searching
structure-based virtual screening
substructure searching

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Related Special Issue

Chemoinformatics in Informatics (13 articles)

Published Papers (12 papers)

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Editorial

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146 KiB

Open AccessEditorial

Special Issue: Chemoinformatics

by Peter Willett

Molecules 2016, 21(4), 535; https://doi.org/10.3390/molecules21040535 - 22 Apr 2016

Cited by 2 | Viewed by 4466

Abstract

Chemoinformatics techniques were originally developed for the construction and searching of large archives of chemical structures but they were soon applied to problems in drug discovery and are now playing an increasingly important role in many additional areas of chemistry. This Special Issue contains seven original research articles and four review articles that provide an introduction to several aspects of this rapidly developing field. Full article

(This article belongs to the Special Issue Chemoinformatics)

Research

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3290 KiB

Open AccessArticle

Nanopore Event-Transduction Signal Stabilization for Wide pH Range under Extreme Chaotrope Conditions

by Stephen Winters-Hilt and Alexander Stoyanov

Molecules 2016, 21(3), 346; https://doi.org/10.3390/molecules21030346 - 14 Mar 2016

Cited by 2 | Viewed by 4764

Abstract

Operation of an α-hemolysin nanopore transduction detector is found to be surprisingly robust over a critical range of pH (6–9), including physiological pH = 7.4 and polymerase chain reaction (PCR) pH = 8.4, and extreme chaotrope concentration, including 5 M urea. The engineered transducer molecule that is captured in the standard α-hemolysin nanopore detector, to transform it into a transduction detector, appears to play a central role in this stabilization process by stabilizing the channel against gating during its capture. This enables the nanopore transduction detector to operate as a single molecule “nanoscope” in a wide range of conditions, where tracking on molecular state is possible in a variety of different environmental conditions. In the case of streptavidin biosensing, results are shown for detector operation when in the presence of extreme (5 M) urea concentration. Complications involving degenerate states are encountered at higher chaotrope concentrations, but since the degeneracy is only of order two, this is easily absorbed into the classification task as in prior work. This allows useful detector operation over a wide range of conditions relevant to biochemistry, biomedical engineering, and biotechnology. Full article

(This article belongs to the Special Issue Chemoinformatics)

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633 KiB

Open AccessArticle

Extended Functional Groups (EFG): An Efficient Set for Chemical Characterization and Structure-Activity Relationship Studies of Chemical Compounds

by Elena S. Salmina, Norbert Haider and Igor V. Tetko

Molecules 2016, 21(1), 1; https://doi.org/10.3390/molecules21010001 - 23 Dec 2015

Cited by 38 | Viewed by 15428

Abstract

The article describes a classification system termed “extended functional groups” (EFG), which are an extension of a set previously used by the CheckMol software, that covers in addition heterocyclic compound classes and periodic table groups. The functional groups are defined as SMARTS patterns and are available as part of the ToxAlerts tool (http://ochem.eu/alerts) of the On-line CHEmical database and Modeling (OCHEM) environment platform. The article describes the motivation and the main ideas behind this extension and demonstrates that EFG can be efficiently used to develop and interpret structure-activity relationship models. Full article

(This article belongs to the Special Issue Chemoinformatics)

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948 KiB

Open AccessArticle

Extremely Randomized Machine Learning Methods for Compound Activity Prediction

by Wojciech M. Czarnecki, Sabina Podlewska and Andrzej J. Bojarski

Molecules 2015, 20(11), 20107-20117; https://doi.org/10.3390/molecules201119679 - 9 Nov 2015

Cited by 8 | Viewed by 6299

Abstract

Speed, a relatively low requirement for computational resources and high effectiveness of the evaluation of the bioactivity of compounds have caused a rapid growth of interest in the application of machine learning methods to virtual screening tasks. However, due to the growth of the amount of data also in cheminformatics and related fields, the aim of research has shifted not only towards the development of algorithms of high predictive power but also towards the simplification of previously existing methods to obtain results more quickly. In the study, we tested two approaches belonging to the group of so-called ‘extremely randomized methods’—Extreme Entropy Machine and Extremely Randomized Trees—for their ability to properly identify compounds that have activity towards particular protein targets. These methods were compared with their ‘non-extreme’ competitors, i.e., Support Vector Machine and Random Forest. The extreme approaches were not only found out to improve the efficiency of the classification of bioactive compounds, but they were also proved to be less computationally complex, requiring fewer steps to perform an optimization procedure. Full article

(This article belongs to the Special Issue Chemoinformatics)

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741 KiB

Open AccessArticle

A Quantum-Based Similarity Method in Virtual Screening

by Mohammed Mumtaz Al-Dabbagh, Naomie Salim, Mubarak Himmat, Ali Ahmed and Faisal Saeed

Molecules 2015, 20(10), 18107-18127; https://doi.org/10.3390/molecules201018107 - 2 Oct 2015

Cited by 21 | Viewed by 5951

Abstract

One of the most widely-used techniques for ligand-based virtual screening is similarity searching. This study adopted the concepts of quantum mechanics to present as state-of-the-art similarity method of molecules inspired from quantum theory. The representation of molecular compounds in mathematical quantum space plays a vital role in the development of quantum-based similarity approach. One of the key concepts of quantum theory is the use of complex numbers. Hence, this study proposed three various techniques to embed and to re-represent the molecular compounds to correspond with complex numbers format. The quantum-based similarity method that developed in this study depending on complex pure Hilbert space of molecules called Standard Quantum-Based (SQB). The recall of retrieved active molecules were at top 1% and top 5%, and significant test is used to evaluate our proposed methods. The MDL drug data report (MDDR), maximum unbiased validation (MUV) and Directory of Useful Decoys (DUD) data sets were used for experiments and were represented by 2D fingerprints. Simulated virtual screening experiment show that the effectiveness of SQB method was significantly increased due to the role of representational power of molecular compounds in complex numbers forms compared to Tanimoto benchmark similarity measure. Full article

(This article belongs to the Special Issue Chemoinformatics)

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2521 KiB

Open AccessArticle

A Database of Force-Field Parameters, Dynamics, and Properties of Antimicrobial Compounds

by Giuliano Malloci, Attilio Vittorio Vargiu, Giovanni Serra, Andrea Bosin, Paolo Ruggerone and Matteo Ceccarelli

Molecules 2015, 20(8), 13997-14021; https://doi.org/10.3390/molecules200813997 - 3 Aug 2015

Cited by 47 | Viewed by 12183

Abstract

We present an on-line database of all-atom force-field parameters and molecular properties of compounds with antimicrobial activity (mostly antibiotics and some beta-lactamase inhibitors). For each compound, we provide the General Amber Force Field parameters for the major species at physiological pH, together with an analysis of properties of interest as extracted from µs-long molecular dynamics simulations in explicit water solution. The properties include number and population of structural clusters, molecular flexibility, hydrophobic and hydrophilic molecular surfaces, the statistics of intraand inter-molecular H-bonds, as well as structural and dynamical properties of solvent molecules within first and second solvation shells. In addition, the database contains several key molecular parameters, such as energy of the frontier molecular orbitals, vibrational properties, rotational constants, atomic partial charges and electric dipole moment, computed by Density Functional Theory. The present database (to our knowledge the first extensive one including dynamical properties) is part of a wider project aiming to build-up a database containing structural, physico-chemical and dynamical properties of medicinal compounds using different force-field parameters with increasing level of complexity and reliability. The database is freely accessible at http://www.dsf.unica.it/translocation/db/. Full article

(This article belongs to the Special Issue Chemoinformatics)

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1693 KiB

Open AccessArticle

Three-Dimensional Compound Comparison Methods and Their Application in Drug Discovery

by Woong-Hee Shin, Xiaolei Zhu, Mark Gregory Bures and Daisuke Kihara

Molecules 2015, 20(7), 12841-12862; https://doi.org/10.3390/molecules200712841 - 16 Jul 2015

Cited by 53 | Viewed by 9194

Abstract

Virtual screening has been widely used in the drug discovery process. Ligand-based virtual screening (LBVS) methods compare a library of compounds with a known active ligand. Two notable advantages of LBVS methods are that they do not require structural information of a target receptor and that they are faster than structure-based methods. LBVS methods can be classified based on the complexity of ligand structure information utilized: one-dimensional (1D), two-dimensional (2D), and three-dimensional (3D). Unlike 1D and 2D methods, 3D methods can have enhanced performance since they treat the conformational flexibility of compounds. In this paper, a number of 3D methods will be reviewed. In addition, four representative 3D methods were benchmarked to understand their performance in virtual screening. Specifically, we tested overall performance in key aspects including the ability to find dissimilar active compounds, and computational speed. Full article

(This article belongs to the Special Issue Chemoinformatics)

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962 KiB

Open AccessArticle

Low-Quality Structural and Interaction Data Improves Binding Affinity Prediction via Random Forest

by Hongjian Li, Kwong-Sak Leung, Man-Hon Wong and Pedro J. Ballester

Molecules 2015, 20(6), 10947-10962; https://doi.org/10.3390/molecules200610947 - 12 Jun 2015

Cited by 78 | Viewed by 7670

Abstract

Docking scoring functions can be used to predict the strength of protein-ligand binding. It is widely believed that training a scoring function with low-quality data is detrimental for its predictive performance. Nevertheless, there is a surprising lack of systematic validation experiments in support of this hypothesis. In this study, we investigated to which extent training a scoring function with data containing low-quality structural and binding data is detrimental for predictive performance. We actually found that low-quality data is not only non-detrimental, but beneficial for the predictive performance of machine-learning scoring functions, though the improvement is less important than that coming from high-quality data. Furthermore, we observed that classical scoring functions are not able to effectively exploit data beyond an early threshold, regardless of its quality. This demonstrates that exploiting a larger data volume is more important for the performance of machine-learning scoring functions than restricting to a smaller set of higher data quality. Full article

(This article belongs to the Special Issue Chemoinformatics)

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Review

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1065 KiB

Open AccessReview

Chemoinformatics: Achievements and Challenges, a Personal View

by Johann Gasteiger

Molecules 2016, 21(2), 151; https://doi.org/10.3390/molecules21020151 - 27 Jan 2016

Cited by 64 | Viewed by 11839

Abstract

Chemoinformatics provides computer methods for learning from chemical data and for modeling tasks a chemist is facing. The field has evolved in the past 50 years and has substantially shaped how chemical research is performed by providing access to chemical information on a scale unattainable by traditional methods. Many physical, chemical and biological data have been predicted from structural data. For the early phases of drug design, methods have been developed that are used in all major pharmaceutical companies. However, all domains of chemistry can benefit from chemoinformatics methods; many areas that are not yet well developed, but could substantially gain from the use of chemoinformatics methods. The quality of data is of crucial importance for successful results. Computer-assisted structure elucidation and computer-assisted synthesis design have been attempted in the early years of chemoinformatics. Because of the importance of these fields to the chemist, new approaches should be made with better hardware and software techniques. Society’s concern about the impact of chemicals on human health and the environment could be met by the development of methods for toxicity prediction and risk assessment. In conjunction with bioinformatics, our understanding of the events in living organisms could be deepened and, thus, novel strategies for curing diseases developed. With so many challenging tasks awaiting solutions, the future is bright for chemoinformatics. Full article

(This article belongs to the Special Issue Chemoinformatics)

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6349 KiB

Open AccessFeature PaperReview

Pharmacophore Models and Pharmacophore-Based Virtual Screening: Concepts and Applications Exemplified on Hydroxysteroid Dehydrogenases

by Teresa Kaserer, Katharina R. Beck, Muhammad Akram, Alex Odermatt and Daniela Schuster

Molecules 2015, 20(12), 22799-22832; https://doi.org/10.3390/molecules201219880 - 19 Dec 2015

Cited by 131 | Viewed by 18905

Abstract

Computational methods are well-established tools in the drug discovery process and can be employed for a variety of tasks. Common applications include lead identification and scaffold hopping, as well as lead optimization by structure-activity relationship analysis and selectivity profiling. In addition, compound-target interactions associated with potentially harmful effects can be identified and investigated. This review focuses on pharmacophore-based virtual screening campaigns specifically addressing the target class of hydroxysteroid dehydrogenases. Many members of this enzyme family are associated with specific pathological conditions, and pharmacological modulation of their activity may represent promising therapeutic strategies. On the other hand, unintended interference with their biological functions, e.g., upon inhibition by xenobiotics, can disrupt steroid hormone-mediated effects, thereby contributing to the development and progression of major diseases. Besides a general introduction to pharmacophore modeling and pharmacophore-based virtual screening, exemplary case studies from the field of short-chain dehydrogenase/reductase (SDR) research are presented. These success stories highlight the suitability of pharmacophore modeling for the various application fields and suggest its application also in futures studies. Full article

(This article belongs to the Special Issue Chemoinformatics)

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3316 KiB

Open AccessReview

Recent Advances in Developing Inhibitors for Hypoxia-Inducible Factor Prolyl Hydroxylases and Their Therapeutic Implications

by So Yeon Kim and Eun Gyeong Yang

Molecules 2015, 20(11), 20551-20568; https://doi.org/10.3390/molecules201119717 - 19 Nov 2015

Cited by 59 | Viewed by 13885

Abstract

Hypoxia-inducible factor (HIF) prolyl hydroxylases (PHDs) are members of the 2-oxoglutarate dependent non-heme iron dioxygenases. Due to their physiological roles in regulation of HIF-1α stability, many efforts have been focused on searching for selective PHD inhibitors to control HIF-1α levels for therapeutic applications. In this review, we first describe the structure of PHD2 as a molecular basis for structure-based drug design (SBDD) and various experimental methods developed for measuring PHD activity. We further discuss the current status of the development of PHD inhibitors enabled by combining SBDD approaches with high-throughput screening. Finally, we highlight the clinical implications of small molecule PHD inhibitors. Full article

(This article belongs to the Special Issue Chemoinformatics)

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Other

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310 KiB

Open AccessOpinion

Chemoinformatics in the New Era: From Molecular Dynamics to Systems Dynamics

by Guanyu Wang

Molecules 2016, 21(3), 71; https://doi.org/10.3390/molecules21030071 - 3 Mar 2016

Cited by 2 | Viewed by 4772

Abstract

Chemoinformatics, due to its power in gathering information at the molecular level, has a wide array of important applications to biology, including fundamental biochemical studies and drug discovery and optimization. As modern “omics” based profiling and network based modeling and simulation techniques grow in sophistication, chemoinformatics now faces a great opportunity to include systems-level control mechanisms as one of its pillar components to extend and refine its various applications. This viewpoint article, through the example of computer aided targeting of the PI3K/Akt/mTOR pathway, outlines major steps of integrating systems dynamics simulations into molecular dynamics simulations to facilitate a higher level of chemoinformatics that would revolutionize drug lead optimization, personalized therapy, and possibly other applications. Full article

(This article belongs to the Special Issue Chemoinformatics)

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